terraform-provider-kubernetes VS k8s-device-plugin

Now, to be honest, I'm not entirely sure/confident how this works. When I've used this kind of setup, I had two separate workspaces: one for setting up EKS and one for setting up Kubernetes within EKS. I'd apply the EKS workspace, first, then use its outputs for the Kubernete's workspace. You can see this pattern is specifically outlined in this EKS/k8s example. The Kubernetes provider docs also explicitly warns against creating the cluster in the same module as the Kubernetes provider. So it appears this may work, but it isn't recommended.

It is a pretty important distinction. Terraform and Kubernetes are fundamentally different in how they work. If you ever try to manage kubernetes state from terraform, it the differences become very obvious: https://github.com/hashicorp/terraform-provider-kubernetes/issues/1367

I'm using the terraform kubernetes-provider and I'd like to translate something like this kubectl command into TF:

After the resources be provisioned, you might want to run terraform apply -refresh-only to refresh your local state as the creation of some resource change the state of others within AWS. Also, state differences on metadata.resource_version of k8s resources almost always show up after an apply. This seems to be related to this issue.

You can open an issue here: https://github.com/hashicorp/terraform-provider-kubernetes/issues

We want to do that, but this issue has been a huge blocker for us. You might not hit it unless you’re using AKS, though.

https://github.com/NVIDIA/k8s-device-plugin/issues/332#issue...

K8s-device-plugin

This framework allows the use of external devices (e.g., NVIDIA GPUs, AMD GPUS, SR-IOV NICs) without modifying core Kubernetes components.

Not talking about MIG. NVIDIA device plugin. https://github.com/NVIDIA/k8s-device-plugin

GPU Operator Device plugin

If you ever tried to use GPU-based instances with AWS ECS, or on EKS using the default Nvidia plugin, you would know that it's not possible to make a task/pod shared the same GPU on an instance. If you want to add more replicas to your service (for redundancy or load balancing), you would need one GPU for each replica.

Some notes on Plex/Emby/Kodi and transcoding. If you want true transcoding with GPU acceleration, you have to have Nvidia GPU or be a k8s device plugin genius. The whole idea of mounting elastic devices in k8s is fairly new and rather complex. In the mean time transcoding is best done on a beefy device with a proper CPU (eg i7) or specifically Nvidia GPU because there are numerous pre-made plugins. I just run Plex and Emby on an old ATX gaming machine without GPU acceleration and it works totally fine. They were barely usable for just me when running on the RPis, wouldn't recommend it unless you can figure out how to mount the correct devices in the pod using a custom raspberry pi device plugin . . . lol good luck! - Arm labs device manager: https://community.arm.com/developer/research/b/articles/posts/a-smarter-device-manager-for-kubernetes-on-the-edge - Deis labs Akri device manager: https://github.com/deislabs/akri - Nvidia GPU plugin: https://github.com/NVIDIA/k8s-device-plugin